Information processing apparatus, information processing method, and storage medium for generating teacher information

ABSTRACT

An information processing apparatus performs estimation processing on supervised data, and stores a relationship between teacher information and an estimation result. When unsupervised data is input, the information processing apparatus searches for supervised data high in degree of similarity in estimation result to unsupervised data, and generates teacher information from an estimation result of unsupervised data based on a relationship between teacher information and an estimation result about the detected supervised data.

BACKGROUND Field of the Disclosure

Aspects of the present disclosure generally relate to a technique togenerate teacher information for data used for machine learning, and,more particularly, to a teacher information generation technique toassign pseudo teacher information to unsupervised data through the useof teacher information about learning data.

Description of the Related Art

In learning in an estimator using machine learning, to produce ahigh-performance estimator, a large amount of supervised data isrequired. However, humans assigning teacher information to a largeamount of data may be a troublesome task and, therefore, may beunrealistic. Accordingly, there is known a method of increasing dataused for learning through the use of a small amount of supervised data.United States Patent Publication Application No. 2014/0177947 discussesa method of increasing learning images by generating a new image whichis obtained by performing deformation of a color space on an existingsupervised image, associating teacher information about the originalsupervised image with the generated new image, and adding thethus-processed new image to a learning image set. This method deformsthe color space of the original supervised image, and is, therefore,unable to be applied to an evaluation target in which teacherinformation varies according to color information. Examples of theevaluation target in which teacher information varies according to colorinformation include the assessment of aesthetic degree of photographsdiscussed in Z. Wang, F. Dolcos, D. Beck, S. Chang, and T. Huang,“Brain-Inspired Deep Networks for Image Aesthetics Assessment”,arXiv:1601.04155, 2016. The aesthetic degree of photographs is an indexindicating the degree of beauty or favorability which humans feel whenviewing a photograph. The aesthetic degree may easily vary according tooptional image processing performed on the original image.

With respect to such a data set in which it is difficult to increaselearning data from existing supervised data, there is known a methodcalled “semi-supervised learning” of using unsupervised data to performlearning. Examples of the semi-supervised learning include a methodcalled “self-training”. Self-training learns an estimation model usingonly previously-prepared supervised data and performs estimationprocessing on unsupervised data with use of the generated estimationmodel. In a case where the reliability of a result of this estimationprocessing exceeds a given threshold value, self-training regards theestimation result as teacher information about an unsupervised image,adds the teacher information to a learning image set, and thenre-performs learning.

Usually, in the case of estimating a classification problem, teacherinformation which is assigned to unsupervised data when the method ofself-training is used is a class label. Therefore, in a classificationproblem, with respect to unsupervised data, in a case where theestimation of a class label to be assigned is erroneous, relearning isperformed with use of erroneous teacher information, and, in a casewhere the estimation of a class label to be assigned is correct, thereoccurs no error from a true value. In the case of estimating aregression problem, teacher information which is assigned tounsupervised data when the method of self-training is used becomes acontinuous value which is a result of estimation. With respect to anestimation result of a continuous value, there occurs an error from atrue value in most cases. Therefore, at the time of relearning, alearning data set including unsupervised data having teacher informationin which an error is included is used to perform learning, and, as aresult, a decrease in accuracy of an estimation model may be caused.

SUMMARY

According to an aspect of the present disclosure, an informationprocessing apparatus includes a first estimation model generation unitconfigured to generate a first estimation model for estimatingevaluation information about input data with use of a plurality ofpieces of learning data having evaluation information as teacherinformation, a first evaluation information estimation unit configuredto estimate evaluation information about each of the plurality of piecesof learning data with use of the first estimation model, associate theestimated evaluation information with corresponding learning data, andstore the estimated evaluation information associated therewith, asecond evaluation information estimation unit configured to estimateevaluation information about unsupervised data with use of the firstestimation model, an association unit configured to associateunsupervised data with learning data based on a degree of similaritybetween an estimation result of evaluation information about thelearning data and an estimation result of evaluation information aboutthe unsupervised data, a setting unit configured to set teacherinformation for unsupervised data based on the learning data associatedwith the unsupervised data, and a second estimation model generationunit configured to generate a second estimation model for estimatingevaluation information about input data with use of a plurality ofpieces of learning data having the teacher information and theunsupervised data with the teacher information set thereto.

Further features of the present disclosure will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a functionalconfiguration of an information processing apparatus.

FIG. 2 is a flowchart illustrating estimation model generationprocessing,

FIG. 3 is a flowchart illustrating search processing forsimilar-in-estimation-result unsupervised data.

FIGS. 4A and 4B are explanatory diagrams of search processing forsimilar-in-estimation-result unsupervised data.

FIG. 5 is a flowchart illustrating verification processing forestimation accuracy.

FIGS. 6A and 6B are explanatory diagrams of processing for generatingteacher information about unsupervised data.

FIG. 7 is a diagram illustrating an example of a functionalconfiguration of an information processing apparatus.

FIG. 8 is a flowchart illustrating estimation model generationprocessing.

FIG. 9 is a flowchart illustrating learning data classificationprocessing.

FIG. 10 is a diagram illustrating an example of a functionalconfiguration of an information processing apparatus.

FIG. 11 is a flowchart illustrating estimation model generationprocessing.

FIGS. 12A and 12B are explanatory diagrams of estimation modelgeneration processing.

FIGS. 13A and 13B are explanatory diagrams of estimation modelgeneration processing.

FIGS. 14A and 14B are explanatory diagrams of estimation modelgeneration processing.

DESCRIPTION OF THE EMBODIMENTS

Various exemplary embodiments, features, and aspects of the disclosurewill he described in detail below with reference to the drawings.

An information processing apparatus according to an exemplary embodimentof the present disclosure learns an estimator which estimatesmultivalued information which is a continuous value. The informationprocessing apparatus automatically appends teacher information tounsupervised data, adds the unsupervised data with the teacherinformation appended thereto to a previously-prepared learning data set,and performs learning. Here, the case of estimating a user satisfactionindex for photographs is described as an example. The user satisfactionindex is an index indicating such a degree of satisfaction as how a userwho has viewed a photograph likes the photograph. For example, the usersatisfaction index is a comprehensive index which is determined based onmultiple factors, such as a subject shown in a photograph, a locationalrelationship of the subject, and coloring of the subject, and aestheticis also a factor involved in the user satisfaction. A photograph whichis used as learning data in the present exemplary embodiment ispreviously evaluated with respect to the degree of satisfaction by aplurality of evaluating persons on a scale of one to X. X is an integer.For example, in a case where X is “3”, the evaluating persons evaluate atarget photograph on a scale of 1 to 3 (for example, good/medium/bad). Ahistogram of X bins which is an evaluation distribution of degrees ofsatisfaction evaluated by a plurality of evaluating persons (evaluationinformation) is used for teacher information about learning data. Thehistogram is previously normalized with the number of evaluating personsin such a manner that the total of values of bins becomes “1”. In thepresent exemplary embodiment, information to be estimated is assumed tobe a normalized histogram which is a user satisfaction index.

The value of each bin of the normalized histogram is a continuous value.Therefore, the estimation of a user satisfaction index serves as aregression problem in which the user satisfaction index is estimated asreal numbers corresponding to the number of bins of the histogram. Inthe case of a regression problem, a continuous value which is anestimation result of unsupervised data may have an error occurring froma true value in most instances. Therefore, in a case where an estimationresult of unsupervised data is used as teacher information insemi-supervised learning, in which unsupervised data is used forlearning, learning would be performed with use of teacher informationcontaining an error, thus leading to a decrease in accuracy of anestimation model.

The information processing apparatus according to the present exemplaryembodiment also performs estimation processing on learning data havingteacher information, and stores a relationship between the teacherinformation and an estimation result containing an error. Theinformation processing apparatus searches for learning data similar inestimation result to unsupervised data, and generates teacherinformation about unsupervised data based on teacher information aboutthe searched-for learning data. This reduces the influence of an errorcaused by estimation processing in teacher information. The informationprocessing apparatus is a computer system including a central processingunit (CPU), a read-only memory (ROM), and a random access memory (RAM).The information processing apparatus implements various functions in thepresent exemplary embodiment by executing a computer program stored inthe ROM with the RAM used as a work area. Furthermore, the technicalscope of the present disclosure is not limited to exemplary embodimentsthereof, but covers things set forth in the claims and the range ofequivalents thereof. While, in the present exemplary embodiment, theterm “data” serving as an evaluation target refers to a photograph, inthe present disclosure, the “data” is not limited to an image.

FIG. 1 is a diagram illustrating an example of a functionalconfiguration of an information processing apparatus according to afirst exemplary embodiment. The information processing apparatus 1000functions as a first estimation model generation unit 110, a learningdata estimation processing unit 120, an unsupervised data estimationprocessing unit 130, a similar-in-estimation-result unsupervised datasearch unit 140, and a teacher information setting unit 150. Moreover,the information processing apparatus 1000 functions as an estimationaccuracy verification unit 160, a learning data addition unit 170, and asecond estimation model generation unit 180. Furthermore, theinformation processing apparatus 1000 includes a first learning data set200, which is used for generation of a first estimation model, and anaccuracy verification data set 700. The first learning data set 200 andthe accuracy verification data set 700 are stored in a predeterminedstorage. To the information processing apparatus 1000 configured asdescribed above, a storage storing an unsupervised data set 400, whichis composed of one or more pieces of unsupervised data, is connected.The information processing apparatus 1000 outputs a second estimationmodel 600 as a processing result. The second estimation model 600 isstored in a predetermined storage.

The first learning data set 200 is a data set composed of a plurality ofpieces of learning data including teacher information. Since a usersatisfaction index, which is information to be estimated in the firstexemplary embodiment, is greatly affected by scene categories of images,learning data included in the first learning data set 200 is setaccording to the scene category of an envisaged input image. The scenecategories of images are classification results that are based onsubjects to be image-captured or image capturing situations.Specifically, the scene categories of images include various categoriesdepending on elements of interest, such as abstract categories,including “landscape” and “portrait”, categories focused on an imagecapturing target, including “firework” and “autumn foliage”, andcategories focused on a situation, including “wedding ceremony” and“athletic meet”. The scene categories allow a plurality of labels to beassigned to one image.

For example, in a case where only images of the landscape category arepresent in a learning image data set, it is difficult to estimate a usersatisfaction index for a portrait image. Therefore, to deal with anyoptional input image, it is desirable that learning images of aplurality of categories be evenly present in the first learning data set200. In a case where there is a prerequisite in which only images of aparticular category are acquired as inputs, if learning data of adifferent category is included in a learning data set, a decrease inestimation accuracy is caused. Therefore, in this case, the firstlearning data set 200 is composed of only images of a target category.

The accuracy verification data set 700 is composed of a plurality ofpieces of data which is not included in the first learning data set 200.Each piece of data included in the accuracy verification data set 700also includes teacher information, as with the first learning data set200. It is desirable that the scene category distribution of an imageset included in the accuracy verification data set 700 he similar to thescene category distribution of an image set included in the firstlearning data set 200.

The first estimation model generation unit 110 performs learning withuse of the first learning data set 200, thus generating a firstestimation model 300. The first estimation model 300 is stored in apredetermined storage. A plurality of learning data constituting thefirst learning data set 200 includes a normalized histogram, which is auser satisfaction index, as teacher information. The first estimationmodel 300 acquires an image as input data, and outputs a normalizedhistogram, which is a user satisfaction index, as an estimation result.

The learning data estimation processing unit (first evaluationinformation estimation) 120 performs estimation processing using thefirst estimation model 300 on all of the pieces of learning dataincluded in the first learning data set 200. The learning dataestimation processing unit 120 stores an estimated user satisfactionindex, which is an estimation result, in association with everycorresponding learning data.

The unsupervised data estimation processing unit (second evaluationinformation estimation unit) 130 receives, as an input, the unsuperviseddata set 400, which is composed of one or more pieces of unsuperviseddata. The unsupervised data estimation processing unit 130 performsestimation processing using the first estimation model 300 on all of thepieces of unsupervised data included in the unsupervised data set 400.The unsupervised data estimation processing unit 130 stores an estimateduser satisfaction index, which is an estimation result, in associationwith every corresponding unsupervised data.

The similar-in-estimation-result unsupervised data search unit(association unit) 140 searches for unsupervised data similar inestimation result to learning data included in the first learning dataset 200, with use of the estimated user satisfaction index of learningdata and the estimated user satisfaction index of unsupervised data. Thesimilar-in-estimation-result unsupervised data search unit 140associates the searched-for unsupervised data and the learning datahaving the highest degree of similarity in estimation result with eachother.

The teacher information setting unit 150 derives and sets teacherinformation about unsupervised data based on a relationship betweenteacher information about the learning data associated with theunsupervised data by the similar-in-estimation-result unsupervised datasearch unit 140 and the estimated user satisfaction index. Theestimation accuracy verification unit 160 adds the unsupervised datawith the teacher information set thereto by the teacher informationsetting unit 150 to learning data included in the first learning dataset 200 and then performs learning, thus verifying the estimationaccuracy of an estimation model output with use of the accuracyverification data set 700. In a case where the estimation accuracy isequal to or greater than a threshold value, the estimation accuracyverification unit 160 adds the unsupervised data to a learning dataaddition list.

The learning data addition unit 170 adds together the unsupervised dataincluded in the learning data addition list generated by the estimationaccuracy verification unit 160 and the learning data included in thefirst learning data set 200, thus generating a second learning data set500. The second estimation model generation unit 180 performs learningwith use of the second learning data set 500, thus generating the secondestimation model 600.

FIG. 2 is a flowchart illustrating estimation model generationprocessing performed by the information processing apparatus 1000configured described above.

In step S201, the first estimation model generation unit 110 performslearning with use of the first learning data set 200, which ispreviously prepared, thus generating the first estimation model 300. Thefirst estimation model generation unit 110 uses known methods to performfeature extraction from learning data and generation of an estimationmodel. For example, in a case where target data is an image, the firstestimation model generation unit 110 uses deep learning as an example ofa learning technique.

In step S202, the learning data estimation processing unit 120 performsestimation processing with use of the first estimation model 300 onlearning data included in the first learning data set 200. The learningdata estimation processing unit 120 stores the estimated usersatisfaction index for every piece of selected learning data. In stepS203, the learning data estimation processing unit 120 searches thefirst learning data set 200 for learning data with respect to which theestimated user satisfaction index has not yet been stored. If learningdata in which the estimated user satisfaction index has not yet beenincluded has been detected (YES in step S203), the processing returns tostep S202, in which the learning data estimation processing unit 120performs learning data estimation processing on the detected learningdata. According to processing in steps S202 and S203, the learning dataestimation processing unit 120 stores a user satisfaction index which isa result obtained by humans actually performing evaluation and theestimated user satisfaction index obtained with use of the firstestimation model 300, with respect to all of the pieces of learning dataincluded in the first learning data set 200.

In parallel with processing performed by the learning data estimationprocessing unit 120 as described above, in step S204, the unsuperviseddata estimation processing unit 130 performs estimation processing withuse of the first estimation model 300 on unsupervised data included inthe unsupervised data set 400, which is previously prepared. Theunsupervised data estimation processing unit 130 stores the usersatisfaction index obtained by estimation processing as the estimateduser satisfaction index of the unsupervised data. In step S205, theunsupervised data estimation processing unit 130 searches theunsupervised data set 400 for unsupervised data in which the estimateduser satisfaction index has not yet been stored. if unsupervised data inwhich the estimated user satisfaction index has not yet been includedhas been detected (YES in step S205), the processing returns to stepS204, in which the unsupervised data estimation processing unit 130performs estimation processing on the detected unsupervised data.According to processing in steps S204 and S205, the unsupervised dataestimation processing unit 130 stores the estimated user satisfactionindex obtained with use of the first estimation model 300, with respectto all of the pieces of unsupervised data included in the unsuperviseddata set 400.

In a case where the estimated user satisfaction index has beencalculated with respect to all of the pieces of learning data (NO instep S203) and the estimated user satisfaction index has been calculatedwith respect to all of the pieces of unsupervised data (NC) in stepS205), the information processing apparatus 1000 proceeds to nextprocessing. In step S206, the similar-in-estimation-result unsuperviseddata search unit 140 searches for unsupervised data similar inestimation result to learning data included in the first learning dataset 200, and associates each piece of unsupervised data with learningdata having the highest degree of similarity in estimation result.Details of search processing for similar-in-estimation-resultunsupervised data in step S206 are described below.

In step S207, the teacher information setting unit 150 derives and setsteacher information about the learning data associated in step S206, asteacher information about unsupervised data. In step S208, theestimation accuracy verification unit 160 verifies the estimationaccuracy of an estimation model obtained in a case where learning isperformed, with use of the accuracy verification data set 700 andunsupervised data. The estimation accuracy verification unit 160 addsunsupervised data to the learning data addition list according toprocessing in step S208. Details of verification processing for theestimation accuracy in step S208 are described below.

In step S209, the learning data addition unit 170 generates the secondlearning data set 500 based on unsupervised data written in the learningdata addition list and learning data included in the first learning dataset 200. In step S210, the second estimation model generation unit 180performs learning using the second learning data set 500, thusgenerating the second estimation model 600. The second estimation modelgeneration unit 180 generates the second estimation model 600 byperforming learning similar to the learning performed in processingperformed in step S201. The second estimation model generation unit 180outputs the generated second estimation model 600.

Furthermore, in a case where no piece of unsupervised data has beenadded to the learning data addition list in processing performed in stepS208, the second learning data set 500 is completely consistent with thefirst learning data set 200. Therethre, an increase in accuracy of anestimation model caused by the addition of unsupervised data to learningdata cannot be expected. In this case, the information processingapparatus 1000 outputs an estimation model generated with use of onlypreviously-prepared supervised data for learning, as the secondestimation model 600.

FIG. 3 is a flowchart illustrating search processing forsimilar-in-estimation-result unsupervised data in step S206. FIGS. 4Aand 4B are explanatory diagrams of search processing forsimilar-in-estimation-result unsupervised data.

In step S301, the similar-in-estimation-result unsupervised data searchunit 140 acquires a plurality of pieces of unsupervised data similar tolearning data on a feature amount basis. The feature amount used forsimilar data search is a feature amount corresponding to information tobe estimated. For example, examples of a feature amount used in the caseof an image include a color histogram and a scale-invariant featuretransform (SIFT) feature amount. Since a user satisfaction, which is anestimation target in the present exemplary embodiment, is an index whichis greatly affected by, for example, a composition of a photograph, anobject shown therein, and coloring thereof, it is effective to use afeature amount obtained by extracting such a factor.

FIG. 4A is a schematic diagram of search processing for similar data ona feature amount basis. FIG. 4A illustrates the manner of performingfeature amount extraction from M pieces of unsupervised data A1 to Amand N pieces of learning data L1 to Ln and calculating the degree ofsimilarity of the extracted feature amount. The degree of similarity infeature amount is expressed by a real number of 0 or more and 1 or less.In a case where the degree of similarity is “1”, two images arecompletely consistent with each other. The similar-in-estimation-resultunsupervised data search unit 140 is able to calculate the degree ofsimilarity in feature amount with use of a similarity index which variesaccording to the extracted feature amount. For example, in a case wherea color histogram feature amount is used, thesimilar-in-estimation-result unsupervised data search unit 140calculates the degree of inter-image similarity with use of a histogramintersection, which is a similarity index between histograms. In a casewhere a SIFT feature amount is used, the similar-in-estimation-resultunsupervised data search unit 140 calculates the degree of inter-imagesimilarity with use of a method called “Bag of Features”.

The similar-in-estimation-result unsupervised data search unit 140selects a pair of unsupervised data and learning data about which thecalculated degree of inter-image similarity is equal to or greater thana threshold value, as a similar image pair. In FIG. 4A, the thresholdvalue is “0.75”. As a pair in which the degree of similarity betweenlearning data and unsupervised data is equal to or greater than thethreshold value, a pair of learning data L1 and unsupervised data A1, apair of learning data L1 and unsupervised data Am, a pair of learningdata L2 and unsupervised data A1, and a pair of learning data Ln andunsupervised data Am are selected. Processing in step S301 is directedto extracting unsupervised data similar to learning data.

In step S302, the similar-in-estimation-result unsupervised data searchunit 140 calculates the degree of similarity in estimated usersatisfaction index with regard to pairs between learning data andunsupervised data acquired in processing performed in step S301. In thefirst exemplary embodiment, since the estimated user satisfaction indexis defined as a normalized histogram, the degree of similarity inestimated user satisfaction index is defined by an inter-histogramdistance. FIG. 4B is a schematic diagram of calculation processing forthe degree of similarity in estimated user satisfaction. FIG. 4Billustrates the manner of calculating the degree of similarity inestimated user satisfaction in each of all of the pairs between learningdata and unsupervised data selected in processing performed in stepS301. For example, according to an estimation result similarity matrix,the degree of similarity between a normalized histogram which is anestimation result of learning data Ln and a normalized histogram whichis an estimation result of unsupervised data Am is calculated to be“0.63”. Similarly, an estimation result (degree of similarity) betweenlearning data L1 and unsupervised data A1 is “0.82”. An estimationresult (degree of similarity) between learning data. L1 and unsuperviseddata Am is “0.85”. An estimation result (degree of similarity) betweenlearning data L2 and unsupervised data A1 is “0.88”.

In step S303, the similar-in-estimation-result unsupervised data searchunit 140 associates each piece of unsupervised data with learning datahaving the highest degree of similarity in estimated user satisfactionindex calculated in processing performed in step S302 (having theshortest inter-histogram distance). In the case of an exampleillustrated in FIG. 4B, unsupervised data A1 is allocated to learningdata L2 highest in estimation result (degree of similarity), andunsupervised data Am is allocated to learning data L1. With regard tounsupervised data A2, since there is no learning data high in degree ofsimilarity, no allocation is performed, so that unsupervised data A2 isnot used as learning data. Moreover, at this time, as in processingperformed in step S301, no allocation is also performed with regard tounsupervised data the estimation result similarity of which is less thanthe threshold value. For example, in a case where the threshold value isset to “0.90”, no allocation is also performed with regard to a pair oflearning data L1 and unsupervised data Am having the highest degree ofsimilarity (degree of similarity being 0.85).

Processing in step S301 has the effect of increasing the accuracy ofteacher information by extracting unsupervised data which is similar indata itself to learning data. For example, in similar data search usingan estimation result in processing performed in step S302, there may becase where there is a plurality of pieces of learning data high inestimation result (degree of similarity) with respect to one piece ofunsupervised data. In this case, depending on information to beestimated, if estimation results similarly resemble each other,selecting pieces of data which are also similar in feature thereofenables assigning teacher information having a smaller amount of error.

A user satisfaction to be estimated in the first exemplary embodiment isgreatly affected by the appearance of an image, such as the location ofa subject shown in an image, a subject itself, and coloring. Therefore,performing processing in steps S301 and S302, which searches for alearning image similar in appearance to an input unsupervised image anduses teacher information about the detected learning image, enablesassigning better teacher information. On the other hand, in a case wherethe association between the degree of similarity of data itself andinformation to be estimated is low, processing in steps S301 and S302can be omitted and searching for learning data similar in estimationresult between all of the pieces of learning data and all of the piecesof unsupervised data can be performed.

FIG. 5 is a flowchart illustrating verification processing forestimation accuracy performed in step S208.

In step S401, the estimation accuracy verification unit 160 performslearning with use of a learning data set including unsupervised data,thus generating an estimation model. The estimation accuracyverification unit 160 selects unsupervised data in which teacherinformation is set in processing performed in steps S206 and S207, fromamong pieces of unsupervised data included in the unsupervised data set400, The estimation accuracy verification unit 160 performs learningwith use of the selected unsupervised data and the first learning dataset 200, thus generating an estimation model.

In step S402, the estimation accuracy verification unit 160 calculatesthe estim n accuracy of the estimation model generated in processingperformed in step S401 with use of the accuracy verification data set700. The accuracy verification data set 700 is composed of a pluralityof pieces of data having teacher information which is not included inthe first learning data set 200. The estimation accuracy is calculatedby performing estimation processing on N pieces of accuracy verificationdata included in the accuracy verification data set 700. Specifically,the estimation accuracy ac of an estimation model is expressed by thefollowing formula.

${ac} = {1 - \frac{\sum\limits_{i = 1}^{N}{D\left( {{h_{gt}(i)},{h_{est}(i)}} \right)}}{N}}$

h_(gt)(i) is teacher information about data i, h_(est)(i) is anestimation result of the data i, D(h1, h2) is a function for calculatingthe distance between a histogram h1 and a histogram h2. In a case wherethe estimation result and the teacher information are consistent witheach other in all of the pieces of accuracy verification data, theestimation accuracy ac is calculated to be “1.0”.

in step S403, the estimation accuracy verification unit 160 checkswhether the estimation accuracy of the estimation model generated inprocessing performed in step S401 is less than a threshold value t. Theinitial value of the threshold value t is, for example, the estimationaccuracy of the first estimation model 300 relative to the accuracyverification data set 700. By performing this processing, in a casewhere the estimation accuracy is reduced by addition of unsuperviseddata, the estimation accuracy verification unit 160 is able to excludethe unsupervised data from learning.

If the estimation accuracy of the estimation model is equal to orgreater than the threshold value t (NO in step S403), then in step S404,the estimation accuracy verification unit 160 updates the thresholdvalue t to the estimation accuracy ac of the estimation model generatedin processing performed in step S401. In step S405, to add unsuperviseddata which has been used for learning to learning data, the estimationaccuracy verification unit 160 adds the unsupervised data to a learningdata addition list. The learning data addition list is a list of piecesof unsupervised data to be newly added as learning data, and is an emptylist in its initial state.

After updating of the learning data addition list or if the estimationaccuracy of the estimation model is less than the threshold value t (YESin step S403), then in step S406, the estimation accuracy verificationunit 160 checks whether there is any unevaluated unsupervised data. Ifteacher information is previously set and there is unsupervised datawhich is not yet subjected to processing in steps S401 to S405 (YES instep S406), the estimation accuracy verification unit 160 repeatedlyperforms processing in step S401 and subsequent steps. If verificationhas been completed with respect to all of the pieces of unsuperviseddata (NO in step S406), the estimation accuracy verification unit 160ends the estimation accuracy verification processing.

The information processing apparatus 1000 according to the firstexemplary embodiment described above automatically assigns teacherinformation to unsupervised data and uses the unsupervised data with theteacher information assigned thereto as learning data, thus being ableto increase variations of learning data and learn a high-accuracyestimator. The information processing apparatus 1000 uses, as teacherinformation about unsupervised data, not an estimation result includingan error but teacher information about learning data similar tounsupervised data, and is, therefore, able to perform learning with useof a value close to a true value which is obtained before an errorcaused by estimation processing is included. Therefore, a deteriorationin learning performance which may be caused by unsupervised data beingused for learning can be reduced.

In a second exemplary embodiment, a configuration for improving thegeneration accuracy of teacher information which is assigned tounsupervised data is described. In the first exemplary embodiment, asteacher information about unsupervised data, teacher information aboutlearning data similar to the unsupervised data is used. However,unsupervised data is not necessarily completely consistent in estimationresult with learning data. Therefore, there occurs an influence of anerror caused by teacher information about similar learning data beingdirectly used. In the case of estimating multivalued data such as a usersatisfaction index in the first exemplary embodiment, assigning the sameteacher information as that of existing learning data to unsuperviseddata causes the same teacher information to exist with respect todifferent pieces of learning data, so that there is a possibility of theoverall estimation accuracy being decreased. Therefore, the informationprocessing apparatus according to the second exemplary embodimentgenerates teacher information about unsupervised data based on arelationship between teacher information about learning data and anestimation result.

The configuration of the information processing apparatus according tothe second exemplary embodiment is similar to that of the inthrmationprocessing apparatus 1000 in the first exemplary embodiment illustratedin FIG. 1, and is, therefore, omitted from description. The estimationmodel generation processing is roughly the same as that of the firstexemplary embodiment illustrated in FIG. 2, but differs in details ofprocessing in steps S202 and S207. This different processing isdescribed. FIGS. 6A and 6B are explanatory diagrams of processing forgenerating teacher information about unsupervised data.

In processing performed in step S202, as in the first exemplaryembodiment, the learning data estimation processing unit 120 performsestimation processing using the first estimation model 300 on learningdata included in the first learning data set 200. At this time, thelearning data estimation processing unit 120 in the second exemplaryembodiment stores, in addition to an estimation result, a relationshipbetween the estimation result and teacher information for every piece oflearning data. The schematic diagram of FIG. 6A illustrates the mannerof calculating an estimation result e by inputting learning data L tothe first estimation model 300.

Here, to enable restoring teacher information gt which is previouslyincluded in the learning data L based on the estimation result e of thelearning data L, the learning data estimation processing unit 120 storesa relationship between the estimation result e and the teacherinformation gt. For example, in a case where the form of information tobe estimated is a normalized histogram with three bins, the learningdata estimation processing unit 120 stores the ratio in every bin of thehistogram between teacher information and an estimation result. In FIG.6A, the teacher information gt about the learning data L is a histogramin which the frequency of bin 1 is “0.2”, the frequency of bin 2 is“0.5”, and the frequency of bin 3 is “0.3”. The estimation result e is ahistogram in which the frequency of bin 1 is “0.25”, the frequency ofbin 2 is “0.45”, and the frequency of bin 3 is “0.3”. The learning dataestimation processing unit 120 stores, as a relationship f between theestimation result e and the teacher information gt, bin 1:0.20/0.25=0.80, bin 2: 0.50/0.45==1.11, and bin 3: 0.30/0.30=1.00, whichare conversion coefficients for the respective bins.

In processing performed in step S207, the teacher information settingunit 150 sets, based on the relationship f between an estimation resultand teacher information stored in processing performed in step S202,teacher information about unsupervised data set in step S206 in learningdata corresponding to the unsupervised data. The schematic diagram ofFIG. 6B illustrates a manner in which learning data L high in estimationresult similarity relative to unsupervised data A is set. The learningdata L includes, as a result of processing in step S202, teacherinformation gt, an estimation result e, and a relationship f between theestimation result e and the teacher information gt. An estimation resulte′ is calculated from the unsupervised data A with use of the firstestimation model 300. In processing performed in step S207, teacherinformation gt′ is calculated from the estimation result e′ according tothe relationship f included in similar learning data.

In a case where the estimation result e′ is a histogram in which thefrequency of bin 1 is “0.22”, the frequency of bin 2 is “0.47”, and thefrequency of bin 3 is “0.31”, when the relationship f stored asconversion coefficients for the respective bins is applied to theestimation result e′, the teacher information gt′ becomes a histogramhaving the following frequencies of the respective bins. The frequencyof bin I is 0.22×0.80=0.176. The frequency of bin 2 is 0.47×1.11=0.522.The frequency of bin 3 is 0.31×1.00=0.310. Moreover, when the calculatedhistogram is normalized, the teacher information gt′ becomes a histogramin which the frequency of bin 1 is “0.175”, the frequency of bin 2 is“0.518”, and the frequency of bin 3 is “0.307”.

Furthermore, in a case where the relationship f is applied to theestimation result e′, weighting of the conversion coefficients can beperformed according to the degree of similarity between the estimationresult e′ about the unsupervised data A and the estimation result eabout the learning data L or the degree of similarity in feature amountcalculated in processing performed in step S206 (step S301 in FIG. 3).For example, in a case where the degree of similarity between theestimation result e′ about the unsupervised data A and the estimationresult e′ about the learning data L is 0.9, weighting can be performedto the conversion coefficients for the respective bins with randomnumbers in the range of 0.9 to 1.1.

The information processing apparatus according to the second exemplaryembodiment described above stores, in advance, a relationship between anestimation result and teacher information relative to learning data, andthen calculates teacher information from an estimation result ofunsupervised data based on the stored relationship. With this, theinformation processing apparatus is able to generate more high-accuracyteacher information about unsupervised data.

A third exemplary embodiment is directed to attaining weight saving of adictionary size and speeding up of processing by performing clusteringon learning data. With this, the information processing apparatus isenabled to shorten a processing time caused by an increase of learningdata. Moreover, the information processing apparatus stores arelationship between an estimation result and teacher information forevery piece of learning data and is, therefore, able to perform weightsaving of a memory size required for processing which becomes enlargeddue to an increase of learning data.

FIG. 7 is a diagram illustrating an example of a functionalconfiguration of an information processing apparatus according to thethird exemplary embodiment. The information processing apparatus 3000has a configuration obtained by adding a learning data classificationunit 330 to the configuration of the information processing apparatus1000 in the first exemplary embodiment illustrated in FIG. 1. Thelearning data classification unit 330 performs classification oflearning data based on an estimation result of each piece of learningdata obtained by the learning data estimation processing unit 120.

FIG. 8 is a flowchart illustrating estimation model generationprocessing which is performed by the information processing apparatus3000. In comparison with the estimation model generation processing inthe first exemplary embodiment illustrated in FIG. 2, processing insteps S801 to S803 is similar to the processing in steps S201 to S203.Processing in steps S805 and S806 is similar to the processing in stepsS204 and S205. Processing in steps S808 to S811 is similar to theprocessing in steps S207 to S210. These similar processing operationsare omitted from description.

According to processing in steps S802 and S803, an estimation result anda relationship between an estimation result and teacher information arestored with respect to all of the pieces of learning data included inthe first learning data set 200. In step S804, the learning dataclassification unit 330 performs learning data classificationprocessing. FIG. 9 is a flowchart illustrating learning dataclassification processing.

In step S901, the learning data classification unit 330 classifieslearning data into a plurality of clusters on a feature amount basis.The feature amount for use in classification is a predetermined featureamount expressing the degree of similarity of the feature itself ofdata, as with the feature amount used in processing performed in stepS301 in the first exemplary embodiment (see FIG. 3). The learning dataclassification unit 330 performs classification processing using a knownmethod. For example, the learning data classification unit 330previously sets the number of clusters into which classification isperformed, and classifies pieces of learning data with use of K-meansclustering. Instead of simply comparing the degrees of similarity of theextracted feature amounts, the learning data classification unit 330 canperform classification with use of a dictionary which has previouslybeen learned by an existing machine learning method. For example, thelearning data classification unit 330 can classify learning images withuse of a dictionary used to perform scene category classification ofimages. According to processing in step S901, pieces of learning dataincluded in the first learning data set 200 are classified into aplurality of clusters corresponding to the degrees of similarity infeature of data.

In step S902, the learning data classification unit 330 determines arepresentative feature amount for every cluster into whichclassification has been performed in processing performed in step S901.The learning data classification unit 330 calculates the average offeature amounts of pieces of learning data included in each cluster, andsets the calculated average feature amount as a representative featureamount. Alternatively, the learning data classification unit 330 can setthe feature amount of learning data having the highest degree ofsimilarity to the average feature amount as a representative featureamount.

In step S903, the learning data classification unit 330 classifieslearning data into a plurality of clusters on an estimation resultbasis. According to processing in steps S802 and S803, an estimationresult is included in each of all of the pieces of learning dataincluded in the first learning data set 200. For every cluster intowhich classification has been performed in processing performed in stepS901, pieces of learning data are further classified into a plurality ofclusters according to the degrees of similarity in estimation result.

In step S904, the learning data classification unit 330 determinesrepresentative data for every cluster into which classification has beenperformed in processing performed in step S903. The learning dataclassification unit 330 sets one piece of learning data selected fromall of the pieces of learning data included in each cluster asrepresentative data. A predetermined method is used for selection oflearning data. For example, the learning data classification unit 330calculates the average of estimation results of pieces of learning dataincluded in each cluster, and sets learning data closest to thecalculated average value as representative data. According to processingin step S904, one piece of learning data is selected as representativedata for every cluster.

According to the learning data classification processing describedabove, all of the pieces of learning data included in the first learningdata set 200 are classified into a plurality of clusters high in degreeof similarity on a feature amount basis and an estimation result basis.Then, representative data retaining teacher information, an estimationresult, and a relationship between an estimation result and teacherinformation is set for every cluster. Learning data which has not beenselected as representative data does not need to contain an estimationresult and a relationship between an estimation result and teacherinformation. Therefore, in comparison with the first exemplaryembodiment and the second exemplary embodiment, the informationprocessing apparatus 3000 according to the third exemplary embodiment isenabled to reduce a memory size required for processing.

After the learning data classification processing is completed and in acase where an estimated user satisfaction index has been calculated withrespect to all of the pieces of unsupervised data (NO in step S806), theinformation processing apparatus 3000 proceeds to next processing. Instep S807, the similar-in-estimation-result unsupervised data searchunit 140 searches for unsupervised data similar to learning data, andsets a pair of learning data and unsupervised data high in degree ofsimilarity. This processing is similar to the processing illustrated inFIG. 3 in the first exemplary embodiment, but, in the third exemplaryembodiment, in similar learning data search processing, thesimilar-in-estimation-result unsupervised data search unit 140 performssearching only on representative data selected in processing performedin step S804. Processing in step S807 is described in detail withreference to the flowchart of FIG. 3 again.

In step S301, the similar-in-estimation-result unsupervised data searchunit 140 searches for a cluster similar to unsupervised data from aplurality of clusters into which learning data has been classified on afeature amount basis in processing performed in step S901. Searching isperformed by comparing a representative feature amount set for eachcluster and a feature amount extracted from unsupervised data with eachother. Unsupervised data and a feature amount basis cluster having adegree of similarity equal to or greater than a threshold value are setas a pair. Furthermore, as in the first exemplary embodiment, thesimilar-in-estimation-result unsupervised data search unit 110 can omitthis processing and can perform selection of similar learning data onlyon an estimation result basis. In that case, feature amount basissimilar learning data classification processing for learning data inprocessing performed in steps S901 and S902 can be omitted.

In step S302, the similar-in-estimation-result unsupervised data searchunit 140 calculates an estimation result similarity between unsuperviseddata set in processing performed in step S301 and representative data ofall of the clusters classified on an estimation result basis included ina cluster to which the unsupervised data has been allocated. In stepS303, the similar-in-estimation-result unsupervised data search unit 140associates representative data highest in estimation result similaritycalculated in processing performed in step S302 with unsupervised data.In comparison with the corresponding processing in the first exemplaryembodiment (processing in step S206 illustrated in FIG. 2), the aboveprocessing in step S807 attains high-speed selection of similar learningdata.

The information processing apparatus 3000 according to the thirdexemplary embodiment described above performs clustering of learningdata on a feature amount basis and an estimation result basis, thusbeing able to attain weight saving of a dictionary size and speeding upof processing time.

In a fourth exemplary embodiment, a method of automatically selecting,from a large number of images owned by the user, an image set high inuser satisfaction and presenting the selected image set to the user isdescribed. The user satisfaction in the fourth exemplary embodiment isan index concerning whether a photograph is good or bad. Whether theuser thinks that a photograph is good is different with each user.Therefore, an information processing apparatus according to the fourthexemplary embodiment is required to perform learning in consideration ofnot only generalized teacher information but also liking of anindividual user.

As a conventional method of extracting an image set high in usersatisfaction, there is a method of performing learning with use of alearning image set in which a satisfaction evaluation distributionobtained by evaluation by a plurality of general users is included inteacher information (evaluation information) and evaluating a user'simage set with use of the generated estimation model. However, in thismethod, in a case where images different in theme or category fromphotographs owned by the user are included in a previously-preparedlearning image set, there is a possibility of selection high in usersatisfaction not being performed with a high degree of accuracy.

For example, in a case where learning is performed with use of alearning data set including a large number of still life photographs orlandscape photographs with respect to a user who likes photographing ofportrait photographs, information specific to still life photographs orlandscape photographs which are not included in an image set owned bythe user is learned. As a result, an estimation accuracy in selection ofportrait photographs which the user likes may be decreased. In thiscase, collecting only a great number of portrait photographs as alearning image set to perform learning enables performing betterlearning. However, it is not easy to previously prepare a great numberof learning images with teacher information made consistent with animage set owned by the user.

To reflect liking of an individual in learning, there is a method ofallowing the user to previously evaluate the degree of satisfaction ofphotographs owned by the user as learning data and using such an imageset for learning. However, a large amount of data is required forlearning, and it is not realistic for the user to prepare and evaluatesuch a large amount of data.

In the fourth exemplary embodiment, learning corresponding to an imageset owned by the user is performed with use of unsupervised images, sothat image selection high in user satisfaction is performed with a highdegree of accuracy. FIG. 10 is a diagram illustrating an example of afunctional configuration of the information processing apparatusaccording to the fourth exemplary embodiment.

The information processing apparatus 4000 functions as a user-specificlearning image set generation unit 410, a first estimation modelgeneration unit 420, a learning image estimation processing unit 430, anunsupervised image estimation processing unit 440, and asimilar-in-estimation-result unsupervised image selection unit 450.Moreover, the information processing apparatus 4000 functions as ateacher information setting unit 460, a learning image addition unit470, a second estimation model generation unit 480, and ahighly-evaluated image selection unit 490. Furthermore, the informationprocessing apparatus 4000 includes an original learning image set 4200,which is composed of a plurality of learning images having teacherinformation. The original learning image set 4200 is stored in apredetermined storage. To the information processing apparatus 4000configured described above, a storage storing a favorite image set 4100,which is composed of a plurality of images high in degree ofsatisfaction previously selected from a plurality of existing imagesowned by the user, an unsupervised image set 4500, and a user image set4800 is connected. Such image sets can be stored in different storages.The information processing apparatus 4000 outputs a highly-evaluatedimage set 4900 as a processing result.

The user-specific learning image set generation unit 410 searches for aplurality of images similar in theme category to the favorite image set4100 from among the previously-prepared original learning image set 4200having teacher information. The user-specific learning image setgeneration unit 410 sets the detected plurality of learning images as afirst learning image set 4300. The user-specific learning image setgeneration unit 410 is equivalent to a user-specific learning data setgeneration unit.

The first estimation model generation unit 420 performs learning withuse of the first learning image set 4300, thus generating a firstestimation model 4400. The first learning image set 4300 is composed ofimages included in the original learning image set 4200. Therefore, eachlearning image is previously actually evaluated by a plurality ofevaluating persons with respect to a degree of satisfaction of thelearning image in multiple stages, and has, as teacher information, anormalized histogram which is a distribution of evaluation results bythe plurality of evaluating persons. The first estimation model 4400receives an image as an input and is able to output, as an estimationresult, a normalized histogram which is a user satisfaction index.

The learning image estimation processing unit 430 performs estimationprocessing on all of the images included in the first learning image set4300 with use of the first estimation model 4400, and stores the outputuser satisfaction index in association with every learning image.

The unsupervised image estimation processing unit 440 receives, asinputs, all of the images included. in the unsupervised image set 4500,which is composed of a plurality of unsupervised images, and performsestimation processing on the received images with use of the firstestimation model 4400. The unsupervised image estimation processing unit440 outputs, as a result of estimation processing, an estimated usersatisfaction index of each unsupervised image.

The similar-in-estimation-result unsupervised image selection unit 450selects unsupervised images in which the degree of similarity thereof inestimation result to learning images included in the first learningimage set 4300 is equal to or greater than a threshold value, from amongunsupervised images included in the unsupervised image set 4500.

The teacher information setting unit 460 updates an estimated usersatisfaction index of each unsupervised image based on a relationshipbetween teacher information and an estimated user satisfaction index ina learning image highest in the degree of similarity in estimationresult to each unsupervised image selected by thesimilar-in-estimation-result unsupervised image selection unit 450.

The learning image addition unit 470 regards the estimated usersatisfaction index updated by the teacher information setting unit 460as teacher information about each unsupervised image, and stores theteacher information in a second learning image set 4600.

The second estimation model generation unit 480 performs learning withuse of the second learning image set 4600. The second estimation modelgeneration unit 480 generates a second estimation model 4700 as a resultof learning.

The highly-evaluated image selection unit 490 evaluates the user imageset 4800 in which photographs taken by the user are included, with useof the second estimation model 4700 generated by the second estimationmodel generation unit 480. The highly-evaluated image selection unit 490outputs, as an evaluation result, photographs high in estimated usersatisfaction as the highly-evaluated image set 4900. The user image set4800 includes a plurality of pieces of unknown image data.

FIG. 11 is a flowchart illustrating estimation model generationprocessing performed by the information processing apparatus 4000configured as described above. FIG. 12 to FIG. 14 are explanatorydiagrams of the estimation model generation processing.

In an initial state, as illustrated in FIG. 12A the original learningimage set 4200, the unsupervised image set 4500, the favorite image set4100, which is an image set previously owned by the user, and the userimage set 4800, which is not yet evaluated, exist. The favorite imageset 4100 includes image data about a plurality of images (an image datagroup) owned by the user. The original learning image set 4200 iscomposed of a plurality of previously-prepared learning images havingteacher information. The unsupervised image set 4500 is composed of aplurality of previously-prepared unsupervised images having no teacherinformation.

The favorite image set 4100 is composed of a plurality of images havingno teacher information high in user satisfaction. The favorite image set4100 can be generated by the user actually selecting images high indegree of satisfaction from among a plurality of images. Alternatively,the favorite image set 4100 can be generated from an image set owned bythe user, based on meta information, such as the number of times animage was used for a photo album generated in the past or the number oftimes an image was viewed. The user image set 4800 is composed of aplurality of images which is not yet evaluated by the user. Theinformation processing apparatus 4000 according to the fourth exemplaryembodiment estimates images high in user satisfaction from among theuser image set 4800 serving as input data, and presents the estimatedimages to the user.

In step S1101, the user-specific learning image set generation unit 410extracts learning images suitable for evaluation of the user image set4800 serving as an evaluation target, from among the original learningimage set 4200. At this time, to understand the tendency of liking ofthe user, the user-specific learning image set generation unit 410 usesthe favorite image set 4100, which was previously generated by the user.The user-specific learning image set generation unit 410 extracts, as auser-specific learning image set, learning images similar in image scenecategory to images included in the favorite image set 4100, from theoriginal learning image set 4200. Classification in scene category ofimages can be performed by using a known method through the use of amachine learning method. With this processing, as illustrated, forexample, in FIG. 12B, a plurality of learning images similar in scenecategory to images included in the favorite image set 4100 is selectedas the first learning image set 4300. In step S1102, as illustrated inFIG. 13A, the first estimation model generation unit 420 generates thefirst estimation model 4400 with use of the first learning image set4300 generated in processing performed in step S1101.

In step S1103, the learning image estimation processing unit 430performs estimation processing on learning images included in the firstlearning image set 4300 with use of the first estimation model 4400. Thelearning image estimation processing unit 430 stores the estimated usersatisfaction index for every selected learning image. Moreover, thelearning image estimation processing unit 430 stores a relationshipbetween teacher information and an estimation result as in the secondexemplary embodiment. In step S1104, the learning image estimationprocessing unit 430 searches for a learning image including no usersatisfaction index from learning images included in the first learningimage set 4300. If a learning image including no user satisfaction indexhas been detected (YES in step S1104), the processing returns to stepS1103, in which the learning image estimation processing unit 430performs estimation processing on the learning image. According toprocessing in steps S1103 and S1104, the learning image estimationprocessing unit 430 calculates a user satisfaction index with respect toeach of all of the learning images included in the first learning imageset 4300. The learning image estimation processing unit 430 causes auser satisfaction index, which is a distribution of evaluation resultsobtained by a plurality of evaluating persons, and an estimated usersatisfaction index, which has been obtained by evaluation using thefirst estimation model 4400, to be included in each of all of thelearning images included in the first learning image set 4300.

In step S1105, the unsupervised image estimation processing unit 440performs estimation processing on arbitrary unsupervised images includedin the unsupervised image set 4500, which is composed of a plurality ofpreviously-prepared images with no teacher information assigned thereto,with use of the first estimation model 4400. The unsupervised imageestimation processing unit 440 stores the estimated user satisfactionindex for every selected unsupervised image. This processing isperformed in parallel with processing performed by the learning imageestimation processing unit 430. In step S1106, the unsupervised imageestimation processing unit 440 searches for an unsupervised image inwhich no user satisfaction index is included from unsupervised imagesincluded in the unsupervised image set 4500. If an unsupervised image inwhich no user satisfaction index is included has been detected (YES instep S1106), the processing returns to step S1105, in which theunsupervised image estimation processing unit 440 performs estimationprocessing on the unsupervised image. According to processing in stepsS1105 and S1106, the unsupervised image estimation processing unit 440calculates a user satisfaction index with respect to each of all of theunsupervised images. The unsupervised image estimation processing unit440 causes the estimated user satisfaction index obtained by estimationusing the first estimation model 4400 to he included in each of all ofthe unsupervised images included in the unsupervised image set 4500.

If the user satisfaction index has been calculated with respect to eachof all of the learning images (NO in step S1104) and the estimated usersatisfaction index has been calculated with respect to each of all ofthe unsupervised images (NO in step S1106), the information processingapparatus 4000 proceeds to next processing. In step S1107, thesimilar-in-estimation-result unsupervised image selection unit 450selects an unsupervised image similar in estimation result to a learningimage. This processing is similar to the processing in step S206 in thefirst exemplary embodiment, and is, therefore, omitted from description.However, in the fourth exemplary embodiment, with respect to anunsupervised image set as a pair with a learning image, in other words,an unsupervised image to be newly added to a learning image set, thesimilar-in-estimation-result unsupervised image selection unit 450 alsocalculates the degree of similarity to all of the images included in thefavorite image set 4100. As illustrated in FIG. 13B, according toprocessing in steps S1103 to S1107, estimation processing is performedon the first learning image set 4300 and the unsupervised image set 4500with use of the first estimation model 4400. With this, an image setsimilar in estimation result to the first learning image set 4300 isselected from the unsupervised image set 4500.

In step S1108, the teacher information setting unit 460 sets teacherinformation about an unsupervised image based on the estimation resultof a learning image allocated to the unsupervised image in step S1107.Details of this processing are similar to those of the processing instep S207 in the second exemplary embodiment, and are, therefore,omitted from description. However, in the fourth exemplary embodiment,the teacher information setting unit 460 performs weighting incalculating teacher information with respect to an unsupervised image inwhich the degree of similarity thereof to images included in thefavorite image set 4100 is equal to or greater than a threshold value,thus performing conversion in such a manner that the user satisfactionindex becomes high.

Optional setting to weighting can be performed according to anestimation target. The teacher information setting unit 460 setsweighting in such a manner that the user satisfaction becomes higherthan in a case where conversion is performed with use of a relationshipbetween teacher information and an estimation result in learning data.For example, in a case where the user satisfaction index is output as ahistogram with three bins, the teacher information setting unit 460re-sets the conversion coefficient of bin 1 (low evaluation) to 90% ofthe original proportion and re-sets the conversion coefficient of bin 3(high evaluation) to 110% of the original proportion.

Weighting is specifically described with reference to FIG. 6B again. Ina case where, in FIG. 6B, the degree of similarity between unsuperviseddata A and an image included in the favorite image set 4100 is equal toor greater than a threshold value, the conversion coefficient of bin 1is changed to “0.72”, which is 90% of the original proportion, and theconversion coefficient of bin 3 is changed to “1.10”, which is 110% ofthe original proportion. With regard to the estimation result e′, thefrequency of bin 1 is “0.22”, the frequency of bin 2 is “0.47”, and thefrequency of bin 3 is “0.31”. With the conversion coefficients changed,the estimation result e′ becomes a histogram in which the frequency ofbin 1 is 0.22×0.72=0.158, the frequency of bin 2 is 0.47×1.11=0.522, andthe frequency of bin 3 is 0.31×1.10=0.341. Moreover, normalizationprocessing is performed, so that, in the histogram of teacherinformation subjected to weighting, the frequency of bin 1 becomes“0.155”, the frequency of bin 2 becomes “0.511”, and the frequency ofbin 3 becomes “0.334”. The user satisfaction becomes “2.18” as expressedby the average value of the histogram. The teacher information gt′illustrated in FIG. 6B is a histogram in which the frequency of bin 1 is“0.175”, the frequency of bin 2 is “0.518”, and the frequency of bin 3is “0.307”, and the average value is “2.13”.

Performing weighting in this way enables setting high the usersatisfaction of an unsupervised image close to an image the user likespersonally. According to this processing, in a case where there is anunsupervised image resembling an image high in user satisfaction, a usersatisfaction index higher than that of the calculated teacherinformation can be set to teacher information based on a relationshipbetween teacher information and an estimation result in learning data.Therefore, selection more suitable for liking of the user becomespossible.

In step S1109, the learning image addition unit 470 adds together theunsupervised images with teacher information set thereto in processingperformed in step S1108 and all of the images included in the firstlearning image set 4300 generated in processing performed in step S1101,thus generating the second learning image set 4600. In step S1110, thesecond estimation model generation unit 480 performs learning with useof the second learning image set 4600, thus generating the secondestimation model 4700. As illustrated in FIG. 14A, according toprocessing in steps S1109 and S1110, the second estimation model 4700 isgenerated from the first learning image set 4300 and an image setincluded in the unsupervised image set 4500 and similar in estimationresult to the first learning image set 4300.

In step S1111, as illustrated in FIG. 14B, the highly-evaluated imageselection unit (presentation unit) 490 performs estimation processing onall of the unknown images included in the user image set 4800 serving asan estimation target with use of the second estimation model 4700, thuscalculating an estimated user satisfaction index. The highly-evaluatedimage selection unit 490 presents images high in estimated usersatisfaction index as the highly-evaluated image set 4900 to the user asan output result. The number of images which are selected as thehighly-evaluated image set 4900 serving as an output result can be agiven number of images previously set in descending order of usersatisfaction or can be the number of all of the images in which the usersatisfaction thereof is equal to or greaterthan a threshold value.

The information processing apparatus 4000 according to the fourthexemplary embodiment described above generates, for every user, alearning image set including unsupervised images based on informationabout a favorite image set previously prepared by the user. Therefore,the information processing apparatus 4000 is enabled to preferentiallypresent an image high in degree of satisfaction from among a new imageset input by the user.

Other Embodiments

Embodiment(s) of the present disclosure can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more frilly as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random access memory (RAM), a read-only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present disclosure has been described with reference toexemplary embodiments, the scope of the following claims are to beaccorded the broadest interpretation so as to encompass all suchmodifications and equivalent structures and functions.

This application claims the benefit of Japanese Patent Application No.2017-214625, filed. Nov. 7, 2017, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An information processing apparatus comprising: afirst estimation model generation unit configured to generate a firstestimation model for estimating evaluation information about input datawith use of a plurality of pieces of learning data having evaluationinformation as teacher information; a first evaluation informationestimation unit configured to estimate evaluation information about eachof the plurality of pieces of learning data with use of the firstestimation model, associate the estimated evaluation information withcorresponding learning data, and store the estimated evaluationinthrmation associated therewith; a second evaluation informationestimation unit configured to estimate evaluation information aboutunsupervised data with use of the first estimation model; an associationunit configured to associate unsupervised data with learning data basedon a degree of similarity between an estimation result of evaluationinformation about the learning data and an estimation result ofevaluation information about the unsupervised data; a setting unitconfigured to set teacher information for unsupervised data based on thelearning data associated with the unsupervised data; and a secondestimation model generation unit configured to generate a secondestimation model for estimating evaluation information about input datawith use of a plurality of pieces of learning data having the teacherinformation and the unsupervised data with the teacher information setthereto.
 2. The information processing apparatus according to claim 1,wherein the setting unit sets teacher information about the learningdata associated with the unsupervised data as teacher information aboutthe unsupervised data.
 3. The information processing apparatus accordingto claim 1, wherein the setting unit derives teacher information for theunsupervised data from an estimation result of evaluation informationabout the unsupervised data based on a relationship between teacherinformation about the learning data associated with the unsuperviseddata and an estimation result of evaluation information about theunsupervised data.
 4. The information processing apparatus according toclaim 3, wherein the association unit associates unsupervised data withlearning data based on a degree of similarity between feature amountsextracted from learning data and unsupervised data.
 5. The informationprocessing apparatus according to claim 4, wherein the setting unitderives teacher information for the unsupervised data from an estimationresult of evaluation information about the unsupervised data based on adegree of similarity in feature amount between learning data andunsupervised data.
 6. The information processing apparatus according toclaim 1, wherein the evaluation information is multivalued informationwhich is a continuous value.
 7. The information processing apparatusaccording to claim 1, further comprising a verification unit configuredto verify performance of an estimation model learned with use of addedunsupervised data.
 8. The information processing apparatus according toclaim 7, wherein the verification unit verifies, with use of newlearning data, the performance of the estimation model learned with useof the added unsupervised data.
 9. The information processing apparatusaccording to claim 7, wherein the verification unit verifies whether theestimation model learned with use of the added unsupervised data isinferior to the first estimation model in performance.
 10. Theinformation processing apparatus according to claim 9, wherein, if it isdetermined that the estimation model learned with use of the addedunsupervised data is inferior to the first estimation model inperformance, the verification unit excludes the added unsupervised datafrom learning data.
 11. The information processing apparatus accordingto claim 1, further comprising a classification unit configured toclassify a plurality of pieces of learning data into a plurality ofclusters based on an estimation result of evaluation information aboutthe learning data, and select, for every cluster, representative datafrom learning data included in the cluster.
 12. The informationprocessing apparatus according to claim 11, wherein the association unitregards only representative data of each cluster as learning data toperform association with unsupervised data.
 13. The informationprocessing apparatus according to claim 1, wherein the evaluationinformation is a normalized histogram.
 14. The information processingapparatus according to claim 1, wherein the data is image data.
 15. Theinformation processing apparatus according to claim 14, wherein theevaluation information is a distribution of satisfaction evaluationperformed by a plurality of users for image data.
 16. The informationprocessing apparatus according to claim 14, further comprising alearning data set generation unit configured to extract, from theplurality of pieces of learning data, image data similar in category toimage data included in an image data group high in user satisfaction,and use a plurality of pieces of extracted learning data as learningdata.
 17. The information processing apparatus according to claim 16,further comprising a presentation unit configured to extract image datahigh in user satisfaction from a plurality of pieces of unknown imagedata with use of the second estimation model, and present the extractedimage data to a user.
 18. The information processing apparatus accordingto claim 16, wherein the setting unit derives teacher information forthe unsupervised data from an estimation result of the unsupervised databased on a degree of similarity between the unsupervised data and anyone of pieces of image data included in the image data group high inuser satisfaction.
 19. An information processing method comprising:generating a first estimation model for estimating evaluationinformation about input data with use of a plurality of pieces oflearning data having evaluation information as teacher information;estimating evaluation information about each of the plurality of piecesof learning data with use of the first estimation model, associating theestimated evaluation information with corresponding learning data, andstoring the estimated evaluation information associated therewith;estimating evaluation information about unsupervised data with use ofthe first estimation model; associating unsupervised data with learningdata based on a degree of similarity between an estimation result ofevaluation information about the learning data and an estimation resultof evaluation information about the unsupervised data; setting teacherinformation for unsupervised data based on the learning data associatedwith the unsupervised data; and generating a second estimation model forestimating evaluation information about input data with use of aplurality of pieces of learning data having the teacher information andthe unsupervised data with the teacher information set thereto.
 20. Anon-transitory computer-readable storage medium storing a computerprogram that causes a computer to function as: a first estimation modelgeneration unit configured to generate a first estimation model forestimating evaluation information about input data with use of aplurality of pieces of learning data having evaluation information asteacher information; a first evaluation information estimation unitconfigured to estimate evaluation information about each of theplurality of pieces of learning data with use of the first estimationmodel, associate the estimated evaluation information with correspondinglearning data, and store the estimated evaluation information associatedtherewith; a second evaluation information estimation unit configured toestimate evaluation information about unsupervised data with use of thefirst estimation model; an association unit configured to associateunsupervised data with learning data based on a degree of similaritybetween an estimation result of evaluation information about thelearning data and an estimation result of evaluation information aboutthe unsupervised data; a setting unit configured to set teacherinformation for unsupervised data based on the learning data associatedwith the unsupervised data; and a second estimation model generationunit configured to generate a second estimation model for estimatingevaluation information about input data with use of a plurality ofpieces of learning data having the teacher information and theunsupervised data with the teacher information set thereto.